feat(v1): 架构与算法优化——语义缓存 2.37x、Router 去重、分类器确定性决胜

架构： - Router：低置信度直连/专家异常两条兜底路径的收尾逻辑（回退→finalize→record）提取为 _fallback_result 公共方法，消除三处重复收尾块算法： - RouterCache：语义条目写入时预计算向量范数（原每次两两比较重算）、语义查找单遍完成（原命中后二次 O(N) 查找）、相似度=1.0 提前终止扫描；微基准（3000 条目×200 查询）：3986ms -> 1685ms，2.37x - RuleClassifier：同分决胜改为按领域名字典序（与规则表排列顺序无关的确定性）、次高分由全排序改 O(n) 扫描测试：新增 5 项（缓存范数一致性/提升后无残留/淘汰同步清理、决胜确定性、区分度惩罚） pytest 25 passed（原 20 全绿 + 新增 5）基线检查点：66b6fd8（操作前已提交，20 passed）
2026-09-18 08:10:02 +08:00
parent 66b6fd8c3e
commit 9fdf91c2fb
6 changed files with 481 additions and 404 deletions
@@ -1,6 +1,42 @@
 from router_system.cache import RouterCache


+def test_semantic_lookup_after_many_entries():
+    """多条目下语义命中正确（范数预计算 + 单遍扫描的回归）。"""
+    c = RouterCache(similarity_threshold=0.5)
+    for i in range(50):
+        c.put(f"完全不相关的查询主题编号{i}关于烹饪的意见", {"response": f"r{i}"})
+    c.put("用 Python 实现快速排序函数", {"response": "code-answer"})
+    level, got = c.get("用 Python 实现快速排序的函数写法")  # 相似但不完全相同
+    assert level in ("semantic", "exact")
+    assert got["response"] == "code-answer"
+
+
+def test_promotion_clears_semantic_state():
+    """提升为精确缓存后，语义列表与范数索引无残留。"""
+    c = RouterCache(promote_frequency=2)
+    c.put("查询甲", {"response": "a"})
+    first = c.get("查询甲")  # 相似度=1.0 计 exact，hits 达阈值即提升
+    assert first is not None and first[0] == "exact"
+    second = c.get("查询甲")
+    assert second is not None and second[0] == "exact"
+    assert c.stats()["exact_size"] == 1
+    assert c.stats()["semantic_size"] == 0
+    assert len(c._sem_norms) == 0
+
+
+def test_semantic_eviction_clears_norms():
+    """语义缓存满员淘汰最旧条目时，向量与范数索引同步清理。"""
+    c = RouterCache(max_semantic=2)
+    c.put("查询一", {"response": "1"})
+    c.put("查询二", {"response": "2"})
+    c.put("查询三", {"response": "3"})  # 淘汰查询一
+    assert len(c._semantic) == 2
+    assert len(c._sem_vecs) == 2
+    assert len(c._sem_norms) == 2
+    assert c.get("查询一") is None
+
+
 def test_exact_hit():
    c = RouterCache()
    result = {"response": "hello", "domain": "general"}
@@ -1,44 +1,63 @@
-"""分类器单元测试。"""
-from router_system.classifier import RuleClassifier
-
-
-def test_code_classification():
-    clf = RuleClassifier()
-    r = clf.classify("用 Python 写一个快速排序函数")
-    assert r.domain == "code"
-    assert r.confidence > 0.7
-
-
-def test_math_classification():
-    clf = RuleClassifier()
-    r = clf.classify("求解方程 x^2 - 5x + 6 = 0")
-    assert r.domain == "math"
-    assert r.confidence > 0.7
-
-
-def test_legal_classification():
-    clf = RuleClassifier()
-    r = clf.classify("劳动合同到期不续签需要支付经济补偿吗")
-    assert r.domain == "legal"
-
-
-def test_medical_classification():
-    clf = RuleClassifier()
-    r = clf.classify("高血压患者日常饮食需要注意什么")
-    assert r.domain == "medical"
-
-
-def test_general_low_confidence():
-    clf = RuleClassifier()
-    r = clf.classify("今天天气怎么样")
-    # 未命中任何领域 -> 低置信度，触发 should_fallback
-    assert r.domain == "general"
-    assert clf.should_fallback(r, 0.6) is True
-
-
-def test_difficulty_estimation():
-    clf = RuleClassifier()
-    easy = clf.classify("1 + 1 = ?")
-    hard = clf.classify("证明费马大定理并推导其推论，给出详细步骤")
-    assert hard.difficulty in ("medium", "hard")
-    assert easy.difficulty == "easy"␍
+"""分类器单元测试。"""
+from router_system.classifier import RuleClassifier
+
+
+def test_tie_break_is_deterministic():
+    """同分决胜：按领域名字典序，与规则表排列顺序无关。"""
+    clf = RuleClassifier()
+    clf.rules = {"zeta": [("x", 1.0)], "alpha": [("x", 1.0)]}
+    r = clf.classify("x")
+    assert r.domain == "alpha"
+
+
+def test_distinctiveness_penalty():
+    """次高分占比高（语义含混）时置信度被压低；单一领域命中不受影响。"""
+    clf = RuleClassifier()
+    clf.rules = {"a": [("kw", 1.0)], "b": [("kw", 0.9)]}
+    r_ambiguous = clf.classify("kw")
+    clf_clear = RuleClassifier()
+    clf_clear.rules = {"a": [("kw", 1.0)], "b": [("other", 0.1)]}
+    r_clear = clf_clear.classify("kw")
+    assert r_clear.confidence > r_ambiguous.confidence
+
+
+def test_code_classification():
+    clf = RuleClassifier()
+    r = clf.classify("用 Python 写一个快速排序函数")
+    assert r.domain == "code"
+    assert r.confidence > 0.7
+
+
+def test_math_classification():
+    clf = RuleClassifier()
+    r = clf.classify("求解方程 x^2 - 5x + 6 = 0")
+    assert r.domain == "math"
+    assert r.confidence > 0.7
+
+
+def test_legal_classification():
+    clf = RuleClassifier()
+    r = clf.classify("劳动合同到期不续签需要支付经济补偿吗")
+    assert r.domain == "legal"
+
+
+def test_medical_classification():
+    clf = RuleClassifier()
+    r = clf.classify("高血压患者日常饮食需要注意什么")
+    assert r.domain == "medical"
+
+
+def test_general_low_confidence():
+    clf = RuleClassifier()
+    r = clf.classify("今天天气怎么样")
+    # 未命中任何领域 -> 低置信度，触发 should_fallback
+    assert r.domain == "general"
+    assert clf.should_fallback(r, 0.6) is True
+
+
+def test_difficulty_estimation():
+    clf = RuleClassifier()
+    easy = clf.classify("1 + 1 = ?")
+    hard = clf.classify("证明费马大定理并推导其推论，给出详细步骤")
+    assert hard.difficulty in ("medium", "hard")
+    assert easy.difficulty == "easy"